1 Introducción al software estadístico `R`

R es un entorno de programación orientado al cálculo, manipulación de datos, y representación gráfica, publicado como software libre con licencia GNU-GPL.

1.1 Empezando con `R`

Obtener el directorio de trabajo o working directory

getwd()

listar los objetos en el espacio de trabajo o workspace

ls()

Definir el working directory

setwd("/Users/dlee")

Ver los últimos comandos utilizados en la consola

history() # display last 25 commands
history(max.show=Inf) # display all previous commands

Guardar el historial de comandos

savehistory(file="myfile") # default is ".Rhistory"

Cargar los commandos guardados en una sesión anterior

loadhistory(file="myfile") # default is ".Rhistory"

Salvar todo el workspace en un fichero .RData

save.image()

Guardar objectos especificos a un fichero (si no se especifica la ruta en el ordenador, se guardará en el directorio actual de trabajo).

save(<object list>,file="myfile.RData")

Cargar un workspace en la sesión

load("myfile.RData")

Salir de R. Por defecto R pregunta si deseas guardar la sesión.

q()

1.2 Instalar y cargar librerías en `R`

install.packages("DAAG") # (Data Analysis And Graphics)

Una vez instalada la libreróa, tenemos que cargarla con el comando library o require

library(DAAG) # or require(DAAG)

1.3 Lectura de datos

Consola de R

x <- c(7.82,8.00,7.95) # c de "combinar"
x

## [1] 7.82 8.00 7.95

Otra forma es mediante la función scan()

x <- scan()  # introducir numeros seguidos de ENTER y terminar con un ENTER
1: 7.82
2: 8.00
3: 7.95
4: 
Read 3 items

Para crear un vector de caracteres ""

id <- c("John","Paul","George","Ringo")

To read a character vector

id <- scan(,"")
1: John
2: Paul
3: George
4: Ringo
5: 
Read 4 items

id

## [1] "John"   "Paul"   "George" "Ringo"

1.4 Importar datos

En ocasiones, necesitaremos leer datos de un fichero independiente. Existen varias formas de hacerlo:

scan() (?scan ver la ayuda)

# creamos el fichero ex.txt
cat("Example:", "2 3 5 7", "11 13 17", file = "ex.txt", sep = "\n") 
scan("ex.txt", skip = 1)

## [1]  2  3  5  7 11 13 17

scan("ex.txt", skip = 1, nlines = 1) # only 1 line after the skipped one

## [1] 2 3 5 7

unlink("ex.data") # tidy up

Existen diferentes formatos (.txt, .csv, .xls, .xlsx, SAS, Stata, etc…)
Alguna librerías de R para importar datos:

library(gdata)
library(foreign)

Generalmente leeros datos en formato .txt o .csv

Descara en el siguiente link los datos cardata aquí

mydata1 = read.table("data/cardata.txt") 
mydata2 = read.csv("data/cardata.csv")

Otros formatos .xls and .xlsx

library(gdata)
mydata3 = read.xls ("cardata/cardata.xls", sheet = 1, header = TRUE)

Minitab, SPSS, SAS or Stata

library(foreign)                   
mydata = read.mtp("mydata.mtp")  # Minitab
mydata = read.spss("myfile", to.data.frame=TRUE) # SPSS
mydata = read.dta("mydata.dta") # Stata

O también

library(Hmisc)
mydata = spss.get("mydata.por", use.value.labels=TRUE)  # SPSS

1.5 Exportar datos

Existen diferentes maneras de exportar datos desde R en diferentes formatos. Para SPSS, SAS y Stata. Por ejemplo, mediante la librería foreign. En Excel, la librería xlsx.
Texto delimitado por tabulaciones:

mtcars
?mtcars    
write.table(mtcars, "cardata.txt", sep="\t")

Hoja decálculo de Excel:

library(xlsx)
write.xlsx(mydata, "mydata.xlsx")

1.6 Vectores

Descargar el siguiente código de R aquí
Crear dos vectores

weight<-c(60,72,57,90,95,72)  
class(weight)

## [1] "numeric"

height<-c(1.75,1.80,1.65,1.90,1.74,1.91)

calcular el Body Mass Index (Índice de masa corporal)

bmi<- weight/height^2
bmi

## [1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630

1.7 Estadística básico

mean, median, st dev, variance

mean(weight) 
median(weight)
sd(weight)
var(weight)

Resumen de un vector

summary(weight)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   57.00   63.00   72.00   74.33   85.50   95.00

o también

min(weight)
max(weight)
range(weight)
sum(weight)
length(weight)

Cuantiles y percentiles

quantile(weight) # por defecto cuantil 25%, 50% y 75%

##   0%  25%  50%  75% 100% 
## 57.0 63.0 72.0 85.5 95.0

quantile(weight,c(0.32,0.57,0.98))

##  32%  57%  98% 
## 67.2 72.0 94.5

Covarianza y correlación

La covarianza (\(\sigma_{xy}\)) indica el grado de variacion conjunta de dos variables aleatorias respecto a sus medias

Si \(\sigma_{xy}> 0\), hay dependencia directa (positiva), es decir, a grandes valores de \(x\) corresponden grandes valores de \(y\).
Si \(\sigma_{xy}= 0\), hay una covarianza 0 se interpreta como la no existencia de una relación lineal entre las dos variables estudiadas.
Si \(\sigma_{xy}< 0\)m hay dependencia inversa o negativa, es decir, a grandes valores de \(x\) corresponden pequeños valores de \(y\).

\[ \rm{Cov}(x,y) = \frac{1}{n}\sum_{i=1}^{n}(x_i - \bar{x})(y_i - \bar{y}) \]

cov(weight,height)

## [1] 0.6773333

El coeficiente de correlacion mide la relacion lineal (positiva o negativa) entre dos variables. Formalmente es el cociente entre la covarianza y el producto de las desviaciones típicas de ambas variables. Siendo \(\sigma_x\) y \(\sigma_y\) las desviaciones estandar y \(\sigma_xy\) la covarianza entre \(x\) e \(y\).

\[ \rho_{xy} = \frac{\sigma_{xy}}{\sigma_x~\sigma_y} \]

cor(weight,height)

## [1] 0.437934

1.8 Vectores caracteres y variables factor

subject <- c("John","Peter","Chris","Tony","Mary","Jane")
sex <- c("MALE","MALE","MALE","MALE","FEMALE","FEMALE")
class(subject)

## [1] "character"

table(sex)

## sex
## FEMALE   MALE 
##      2      4

1.9 Data frames

Dat <- data.frame(subject,sex,weight,height)
# añadir el bmi a Dat
Dat$bmi <- bmi  # o Dat$bmi <- weight/height^2
class(Dat)

## [1] "data.frame"

str(Dat) # Ver la estructura del data.frame

## 'data.frame':    6 obs. of  5 variables:
##  $ subject: Factor w/ 6 levels "Chris","Jane",..: 3 5 1 6 4 2
##  $ sex    : Factor w/ 2 levels "FEMALE","MALE": 2 2 2 2 1 1
##  $ weight : num  60 72 57 90 95 72
##  $ height : num  1.75 1.8 1.65 1.9 1.74 1.91
##  $ bmi    : num  19.6 22.2 20.9 24.9 31.4 ...

# cambiar el nombre de las filas
rownames(Dat)<-c("A","B","C","D","E","F")

# Acceder a los elementos del data.frame
Dat[,1]     # columna 1

## [1] John  Peter Chris Tony  Mary  Jane 
## Levels: Chris Jane John Mary Peter Tony

Dat[,1:3]   # columnas 1 a 3

##   subject    sex weight
## A    John   MALE     60
## B   Peter   MALE     72
## C   Chris   MALE     57
## D    Tony   MALE     90
## E    Mary FEMALE     95
## F    Jane FEMALE     72

Dat[1:2,]   # filas 1 a 2

##   subject  sex weight height      bmi
## A    John MALE     60   1.75 19.59184
## B   Peter MALE     72   1.80 22.22222

1.10 Trabajando con data frames

Ejemplo: analizar datos por grupos

Obtener el peso (weight), altura (height) y bmi por FEMALES y MALES:

Seleccionado cada grupo y calculando la media por grupos

Dat[sex=="MALE",]
Dat[sex=="FEMALE",]

mean(Dat[sex=="MALE",3])  # weight average of MALEs
mean(Dat[sex=="MALE","weight"])

Mediante la función apply por columnas

apply(Dat[sex=="FEMALE",3:5],2,mean)
apply(Dat[sex=="MALE",3:5],2,mean)

# podemos utilizar la función apply con cualquier función
apply(Dat[sex=="FEMALE",3:5],2,function(x){x+2})

función by o colMeans

# 'by' divide los datos en factores y realiza 
#   loscálculos para cada grupo
by(Dat[,3:5],sex, colMeans)

función aggregate

# otra opcion
aggregate(Dat[,3:5], by=list(sex),mean)

1.11 Vectores lógicos

Elegir los individuos con BMI>22

bmi
bmi>22
as.numeric(bmi>22) # convierte a numerico 0/1
which(bmi>22)  # nos devuelve la posicion del valor donde bmi>22

Qué valores están entre 20 y 25?

bmi > 20 & bmi < 25
which(bmi > 20 & bmi < 25)

1.12 Trabajando con vectores

Concatenar

x <- c(2, 3, 5, 2, 7, 1)
y <- c(10, 15, 12)
z <- c(x,y)  # concatena x e y

Lista de 2 vectores

zz <- list(x,y) # create a list
unlist(zz) # unlist the list converting it to a concatenated vector

## [1]  2  3  5  2  7  1 10 15 12

Subconjunto de vectores

x[c(1,3,4)]

## [1] 2 5 2

x[-c(2,6)] # simbolo - omite los elementos

## [1] 2 5 2 7

Secuencias

seq(1,9) # o 1:9

## [1] 1 2 3 4 5 6 7 8 9

seq(1,9,by=1)

## [1] 1 2 3 4 5 6 7 8 9

seq(1,9,by=0.5)

##  [1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0

seq(1,9,length=20)

##  [1] 1.000000 1.421053 1.842105 2.263158 2.684211 3.105263 3.526316
##  [8] 3.947368 4.368421 4.789474 5.210526 5.631579 6.052632 6.473684
## [15] 6.894737 7.315789 7.736842 8.157895 8.578947 9.000000

Réplicas

oops <- c(7,9,13)
rep(oops,3) # repite el vector "oops" 3 veces
rep(oops,1:3) # repite cada elemento del vector las veces indicadas

rep(c(2,3,5), 4)
rep(1:2,c(10,15))

rep(c("MALE","FEMALE"),c(4,2)) # también funciona con caracteres
c(rep("MALE",3), rep("FEMALE",2))

1.13 Matrices y arrays

x<- 1:12
x

##  [1]  1  2  3  4  5  6  7  8  9 10 11 12

dim(x)<-c(3,4)  # 3 filas y 4 columnas

X <- matrix(1:12,nrow=3,byrow=TRUE)
X

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]    5    6    7    8
## [3,]    9   10   11   12

X <- matrix(1:12,nrow=3,byrow=FALSE)
X

##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12

# rownames, colnames

rownames(X) <- c("A","B","C")
X

##   [,1] [,2] [,3] [,4]
## A    1    4    7   10
## B    2    5    8   11
## C    3    6    9   12

colnames(X) <- LETTERS[4:7]
X

##   D E F  G
## A 1 4 7 10
## B 2 5 8 11
## C 3 6 9 12

colnames(X) <- month.abb[4:7]
X

##   Apr May Jun Jul
## A   1   4   7  10
## B   2   5   8  11
## C   3   6   9  12

Concatenar filas y columnas rbind(), cbind()

Y <- matrix(0.1*(1:12),3,4)

cbind(X,Y)  # bind column-wise

##   Apr May Jun Jul                
## A   1   4   7  10 0.1 0.4 0.7 1.0
## B   2   5   8  11 0.2 0.5 0.8 1.1
## C   3   6   9  12 0.3 0.6 0.9 1.2

rbind(X,Y)  # bind row-wise

##   Apr May Jun  Jul
## A 1.0 4.0 7.0 10.0
## B 2.0 5.0 8.0 11.0
## C 3.0 6.0 9.0 12.0
##   0.1 0.4 0.7  1.0
##   0.2 0.5 0.8  1.1
##   0.3 0.6 0.9  1.2

1.14 Factors

gender<-c(rep("female",691),rep("male",692))
class(gender)

## [1] "character"

# cambiar vector a factor (por ejemplo a una categoria)
gender<- factor(gender)
levels(gender)

## [1] "female" "male"

summary(gender)

## female   male 
##    691    692

table(gender)

## gender
## female   male 
##    691    692

status<- c(0,3,2,1,4,5)    # Crear vector numerico, 
                           #    transformarlo a niveles.
fstatus <- factor(status, levels=0:5)
levels(fstatus) <- c("student","engineer","unemployed","lawyer","economist","dentist")

Dat$status <- fstatus
Dat

##   subject    sex weight height      bmi     status
## A    John   MALE     60   1.75 19.59184    student
## B   Peter   MALE     72   1.80 22.22222     lawyer
## C   Chris   MALE     57   1.65 20.93664 unemployed
## D    Tony   MALE     90   1.90 24.93075   engineer
## E    Mary FEMALE     95   1.74 31.37799  economist
## F    Jane FEMALE     72   1.91 19.73630    dentist

1.15 Indexando vectores con condiciones lógicas

a <- c(1,2,3,4,5)
b <- c(TRUE,FALSE,FALSE,TRUE,FALSE)

max(a[b])

## [1] 4

sum(a[b])

## [1] 5

1.16 Valores faltantes

En R, los valores faltante (o missing values) se representan como NA (not available). Los valores imposibles (e.g., valores dividos por cero) se representan con el simbolo NaN (not a number).

a <- c(1,2,3,4,NA)
sum(a)

## [1] NA

El argumento na.rm=TRUE excluye los valores NA en el cálculo de algunos valores

sum(a,na.rm=TRUE)

## [1] 10

a <- c(1,2,3,4,NA)
is.na(a) # YES or NO

## [1] FALSE FALSE FALSE FALSE  TRUE

La función complete.cases() devuelve un vector lógico que indica los casos completos.

complete.cases(a)

## [1]  TRUE  TRUE  TRUE  TRUE FALSE

La función na.omit() devuelve un objeto sin los elementos NA.

na.omit(a)

## [1] 1 2 3 4
## attr(,"na.action")
## [1] 5
## attr(,"class")
## [1] "omit"

NA en data frames:

require(graphics)
?airquality
pairs(airquality, panel = panel.smooth, main = "airquality data")

ok <- complete.cases(airquality)
airquality[ok,]

1.17 Trabajando con data frames

Los data frame se utilizand para guardas tablas de datos. Contiene elementos de la misma longitud.

mtcars
?mtcars       # help(mtcars)

Observemos las primeras filas

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Estructura de un data frame

str(mtcars) # display the structure of the data frame

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

Select a car model:

mtcars["Mazda RX4",] # using rows and columns names

##           mpg cyl disp  hp drat   wt  qsec vs am gear carb
## Mazda RX4  21   6  160 110  3.9 2.62 16.46  0  1    4    4

mtcars[c("Datsun 710", "Camaro Z28"),]

##             mpg cyl disp  hp drat   wt  qsec vs am gear carb
## Datsun 710 22.8   4  108  93 3.85 2.32 18.61  1  1    4    1
## Camaro Z28 13.3   8  350 245 3.73 3.84 15.41  0  0    3    4

O variables concretas

mtcars[,c("mpg","am")]

##                      mpg am
## Mazda RX4           21.0  1
## Mazda RX4 Wag       21.0  1
## Datsun 710          22.8  1
## Hornet 4 Drive      21.4  0
## Hornet Sportabout   18.7  0
## Valiant             18.1  0
## Duster 360          14.3  0
## Merc 240D           24.4  0
## Merc 230            22.8  0
## Merc 280            19.2  0
## Merc 280C           17.8  0
## Merc 450SE          16.4  0
## Merc 450SL          17.3  0
## Merc 450SLC         15.2  0
## Cadillac Fleetwood  10.4  0
## Lincoln Continental 10.4  0
## Chrysler Imperial   14.7  0
## Fiat 128            32.4  1
## Honda Civic         30.4  1
## Toyota Corolla      33.9  1
## Toyota Corona       21.5  0
## Dodge Challenger    15.5  0
## AMC Javelin         15.2  0
## Camaro Z28          13.3  0
## Pontiac Firebird    19.2  0
## Fiat X1-9           27.3  1
## Porsche 914-2       26.0  1
## Lotus Europa        30.4  1
## Ford Pantera L      15.8  1
## Ferrari Dino        19.7  1
## Maserati Bora       15.0  1
## Volvo 142E          21.4  1

library(psych)
describe(mtcars)

##      vars  n   mean     sd median trimmed    mad   min    max  range  skew
## mpg     1 32  20.09   6.03  19.20   19.70   5.41 10.40  33.90  23.50  0.61
## cyl     2 32   6.19   1.79   6.00    6.23   2.97  4.00   8.00   4.00 -0.17
## disp    3 32 230.72 123.94 196.30  222.52 140.48 71.10 472.00 400.90  0.38
## hp      4 32 146.69  68.56 123.00  141.19  77.10 52.00 335.00 283.00  0.73
## drat    5 32   3.60   0.53   3.70    3.58   0.70  2.76   4.93   2.17  0.27
## wt      6 32   3.22   0.98   3.33    3.15   0.77  1.51   5.42   3.91  0.42
## qsec    7 32  17.85   1.79  17.71   17.83   1.42 14.50  22.90   8.40  0.37
## vs      8 32   0.44   0.50   0.00    0.42   0.00  0.00   1.00   1.00  0.24
## am      9 32   0.41   0.50   0.00    0.38   0.00  0.00   1.00   1.00  0.36
## gear   10 32   3.69   0.74   4.00    3.62   1.48  3.00   5.00   2.00  0.53
## carb   11 32   2.81   1.62   2.00    2.65   1.48  1.00   8.00   7.00  1.05
##      kurtosis    se
## mpg     -0.37  1.07
## cyl     -1.76  0.32
## disp    -1.21 21.91
## hp      -0.14 12.12
## drat    -0.71  0.09
## wt      -0.02  0.17
## qsec     0.34  0.32
## vs      -2.00  0.09
## am      -1.92  0.09
## gear    -1.07  0.13
## carb     1.26  0.29

2 Análisis de datos básico en `R`

2.1 Gráficos sencillos

Scatterplot

attach(mtcars)
plot(wt, mpg, main="Scatterplot Example",
   xlab="Car Weight ", ylab="Miles Per Gallon ", pch=19)

Matriz scatterplot

pairs(~mpg+disp+drat+wt,data=mtcars,
   main="Simple Scatterplot Matrix")

Barplot o diagrama de barras

tab <- table(mtcars[,c("cyl")])
barplot(tab)

Piechart o diagrama de tarta

pie(tab)

Ejercicio:

El data.frame VADeaths contiene las tasas de mortalidad por cada 1000 habitantes en Virginia (EEUU) en 1940

Las tasas de mortalidad se miden cada 1000 habitantes por año. Se encuentran clasificadas por grupo de edad (filas) y grupo de población (columnas). Los grupos de edad son: 50-54, 55-59, 60-64, 65-69, 70-74 y los grupos de población: Rural/Male, Rural/Female, Urban/Male and Urban/Female.

data(VADeaths)
VADeaths

##       Rural Male Rural Female Urban Male Urban Female
## 50-54       11.7          8.7       15.4          8.4
## 55-59       18.1         11.7       24.3         13.6
## 60-64       26.9         20.3       37.0         19.3
## 65-69       41.0         30.9       54.6         35.1
## 70-74       66.0         54.3       71.1         50.0

Calcula la media para cada grupo de edad.
- Result:

##  50-54  55-59  60-64  65-69  70-74 
## 11.050 16.925 25.875 40.400 60.350

Calcula la media para cada grupo de población.
- Resultado:

##   Rural Male Rural Female   Urban Male Urban Female 
##        32.74        25.18        40.48        25.28

El data.frame rainforest contiene diferentes variables de species

library(DAAG)
rainforest
?rainforest
names(rainforest)

Crear una tabla de conteos para cada species y realiza un gráfico descriptivo.
- Resultado:

## 
## Acacia mabellae      C. fraseri  Acmena smithii   B. myrtifolia 
##              16              12              26              11

El data.frame Acmena está creado a partir de rainforest mediante la función subset.

Realiza un gráfico que relacione la biomasa de la madera (wood) y el diámetro a la altura del pecho (dbh). Utiliza también la escala logarítmica.

Acmena <- subset(rainforest, species == "Acmena smithii")

Calcula un histograma de la variable dbh mediante la función hist

Crea un vector de números enteros positivos impares the longitud 100 y calcula los valores entre 60 y 80.
- Result:

##  [1] 61 63 65 67 69 71 73 75 77 79

Soluciones aquí

2.2 Scatterplots

library(MASS)
data("mammals")
?mammals
head(mammals)

##                    body brain
## Arctic fox        3.385  44.5
## Owl monkey        0.480  15.5
## Mountain beaver   1.350   8.1
## Cow             465.000 423.0
## Grey wolf        36.330 119.5
## Goat             27.660 115.0

attach(mammals)
species <- row.names(mammals)
x <- body
y <- brain

library(calibrate)
# scatterplot
plot(x,y, xlab = "body weight in kgr", ylab = "brain weight in gr", 
     main="Body vs Brain weight \n for 62 Species of Land Mammals",xlim=c(0,8500))
textxy(x,y,labs=species,col = "blue",cex=0.85)

Identificar un punto en el scatterplot

identify(x,y,species)

En escala logarítmica

plot(log(x),log(y), xlab = "log body weight in kgr", ylab = "log brain weight in gr", 
     main="log Body vs log Brain weight \n for 62 Species of Land Mammals")
textxy(log(x),log(y),labs=species,col = "blue",cex=0.85)

Identificar un punto en la escala logarítmica

identify(log(x),log(y),species)

2.3 más opciones gráficas

Varios conjuntos de datos en un sólo gráfico

Una vez realizado un plot, el comando points permite aadir nuevas observaciones.

set.seed(1234)
 x <- rnorm(10,sd=5,mean=20)
 y <- 2.5*x - 1.0 + rnorm(10,sd=9,mean=0)
 cor(x,y)

## [1] 0.7512194

 plot(x,y,xlab="Independent",ylab="Dependent",main="Random plot")
 x1 <- runif(8,15,25)
 y1 <- 2.5*x1 - 1.0 + runif(8,-6,6)
 points(x1,y1,col=2)

con la leyenda

set.seed(1234)
x2 <- runif(8,15,25)
y2 <- 2.5*x2 - 1.0 + runif(8,-6,6)
 plot(x,y,xlab="Independent",ylab="Dependent",main="Random plot")
 points(x1,y1,col=2,pch=3)
 points(x2,y2,col=4,pch=5)
 legend("topleft",c("Original","one","two"),col=c(1,2,4),pch=c(1,3,5))

Varios gráficos en un sóla imagen

set.seed(1234)
 par(mfrow=c(2,3))
 boxplot(rnorm(100),main="first plot")
 boxplot(rgamma(100,2),main="second plot", horizontal=TRUE,col="bisque")
 plot(rnorm(100),xlab="third plot",
      ylab="y-label",main="x-label")
 hist(rnorm(100),main="fourth plot",col="lightgrey")
 hist(rexp(100),main="fifth plot",col="blue")
 plot(rnorm(100),rexp(100),main="sixth plot")

Relaciones entre variables

uData <- rnorm(20)
vData <- rnorm(20,mean=5)
wData <- uData + 2*vData + rnorm(20,sd=0.5)
xData <- -2*uData+rnorm(20,sd=0.1)
yData <-  3*vData+rnorm(20,sd=2.5)
d <- data.frame(u=uData,v=vData,w=wData,x=xData,y=yData)
pairs(d)

Gráfico de correlaciones

La función corrplot de la librería corrplot permite visualizar una matriz de correlaciones calculada mediante la función cor

library(corrplot)
M <- cor(d)
corrplot(M, method="circle",type="upper")

Gráficos de superficies: image, contour y persp

x <- seq(0,2*pi,by=pi/50)
y <- x
xg <- (x*0+1) %*% t(y)
yg <- (x) %*% t(y*0+1)
f <- sin(xg*yg)

par(mfrow=c(2,2))
image(x,y,f)
contour(x,y,f)
contour(x,y,f,nlevels=4)
image(x,y,f,col=grey.colors(100))
contour(x,y,f,nlevels=4,add=TRUE,col="red")

Podemos utilizar la función persp

persp(x,y,f,theta=-30,phi=55,col="lightgrey",shade=.01)

o representar imágenes

library(fields)
data(lennon)
image(lennon,col=grey(seq(0,1,l=256)))

2.4 Tablas de clasificación cruzada o de contigencia

library(MASS)
data(quine)
quine

##     Eth Sex Age Lrn Days
## 1     A   M  F0  SL    2
## 2     A   M  F0  SL   11
## 3     A   M  F0  SL   14
## 4     A   M  F0  AL    5
## 5     A   M  F0  AL    5
## 6     A   M  F0  AL   13
## 7     A   M  F0  AL   20
## 8     A   M  F0  AL   22
## 9     A   M  F1  SL    6
## 10    A   M  F1  SL    6
## 11    A   M  F1  SL   15
## 12    A   M  F1  AL    7
## 13    A   M  F1  AL   14
## 14    A   M  F2  SL    6
## 15    A   M  F2  SL   32
## 16    A   M  F2  SL   53
## 17    A   M  F2  SL   57
## 18    A   M  F2  AL   14
## 19    A   M  F2  AL   16
## 20    A   M  F2  AL   16
## 21    A   M  F2  AL   17
## 22    A   M  F2  AL   40
## 23    A   M  F2  AL   43
## 24    A   M  F2  AL   46
## 25    A   M  F3  AL    8
## 26    A   M  F3  AL   23
## 27    A   M  F3  AL   23
## 28    A   M  F3  AL   28
## 29    A   M  F3  AL   34
## 30    A   M  F3  AL   36
## 31    A   M  F3  AL   38
## 32    A   F  F0  SL    3
## 33    A   F  F0  AL    5
## 34    A   F  F0  AL   11
## 35    A   F  F0  AL   24
## 36    A   F  F0  AL   45
## 37    A   F  F1  SL    5
## 38    A   F  F1  SL    6
## 39    A   F  F1  SL    6
## 40    A   F  F1  SL    9
## 41    A   F  F1  SL   13
## 42    A   F  F1  SL   23
## 43    A   F  F1  SL   25
## 44    A   F  F1  SL   32
## 45    A   F  F1  SL   53
## 46    A   F  F1  SL   54
## 47    A   F  F1  AL    5
## 48    A   F  F1  AL    5
## 49    A   F  F1  AL   11
## 50    A   F  F1  AL   17
## 51    A   F  F1  AL   19
## 52    A   F  F2  SL    8
## 53    A   F  F2  SL   13
## 54    A   F  F2  SL   14
## 55    A   F  F2  SL   20
## 56    A   F  F2  SL   47
## 57    A   F  F2  SL   48
## 58    A   F  F2  SL   60
## 59    A   F  F2  SL   81
## 60    A   F  F2  AL    2
## 61    A   F  F3  AL    0
## 62    A   F  F3  AL    2
## 63    A   F  F3  AL    3
## 64    A   F  F3  AL    5
## 65    A   F  F3  AL   10
## 66    A   F  F3  AL   14
## 67    A   F  F3  AL   21
## 68    A   F  F3  AL   36
## 69    A   F  F3  AL   40
## 70    N   M  F0  SL    6
## 71    N   M  F0  SL   17
## 72    N   M  F0  SL   67
## 73    N   M  F0  AL    0
## 74    N   M  F0  AL    0
## 75    N   M  F0  AL    2
## 76    N   M  F0  AL    7
## 77    N   M  F0  AL   11
## 78    N   M  F0  AL   12
## 79    N   M  F1  SL    0
## 80    N   M  F1  SL    0
## 81    N   M  F1  SL    5
## 82    N   M  F1  SL    5
## 83    N   M  F1  SL    5
## 84    N   M  F1  SL   11
## 85    N   M  F1  SL   17
## 86    N   M  F1  AL    3
## 87    N   M  F1  AL    4
## 88    N   M  F2  SL   22
## 89    N   M  F2  SL   30
## 90    N   M  F2  SL   36
## 91    N   M  F2  AL    8
## 92    N   M  F2  AL    0
## 93    N   M  F2  AL    1
## 94    N   M  F2  AL    5
## 95    N   M  F2  AL    7
## 96    N   M  F2  AL   16
## 97    N   M  F2  AL   27
## 98    N   M  F3  AL    0
## 99    N   M  F3  AL   30
## 100   N   M  F3  AL   10
## 101   N   M  F3  AL   14
## 102   N   M  F3  AL   27
## 103   N   M  F3  AL   41
## 104   N   M  F3  AL   69
## 105   N   F  F0  SL   25
## 106   N   F  F0  AL   10
## 107   N   F  F0  AL   11
## 108   N   F  F0  AL   20
## 109   N   F  F0  AL   33
## 110   N   F  F1  SL    5
## 111   N   F  F1  SL    7
## 112   N   F  F1  SL    0
## 113   N   F  F1  SL    1
## 114   N   F  F1  SL    5
## 115   N   F  F1  SL    5
## 116   N   F  F1  SL    5
## 117   N   F  F1  SL    5
## 118   N   F  F1  SL    7
## 119   N   F  F1  SL   11
## 120   N   F  F1  SL   15
## 121   N   F  F1  AL    5
## 122   N   F  F1  AL   14
## 123   N   F  F1  AL    6
## 124   N   F  F1  AL    6
## 125   N   F  F1  AL    7
## 126   N   F  F1  AL   28
## 127   N   F  F2  SL    0
## 128   N   F  F2  SL    5
## 129   N   F  F2  SL   14
## 130   N   F  F2  SL    2
## 131   N   F  F2  SL    2
## 132   N   F  F2  SL    3
## 133   N   F  F2  SL    8
## 134   N   F  F2  SL   10
## 135   N   F  F2  SL   12
## 136   N   F  F2  AL    1
## 137   N   F  F3  AL    1
## 138   N   F  F3  AL    9
## 139   N   F  F3  AL   22
## 140   N   F  F3  AL    3
## 141   N   F  F3  AL    3
## 142   N   F  F3  AL    5
## 143   N   F  F3  AL   15
## 144   N   F  F3  AL   18
## 145   N   F  F3  AL   22
## 146   N   F  F3  AL   37

attach(quine)
table(Sex)

## Sex
##  F  M 
## 80 66

table(Sex,Age)

##    Age
## Sex F0 F1 F2 F3
##   F 10 32 19 19
##   M 17 14 21 14

# or xtabs
xtabs(~Sex+Age,data=quine)

##    Age
## Sex F0 F1 F2 F3
##   F 10 32 19 19
##   M 17 14 21 14

xtabs(~Sex+Age+Eth,data=quine)

## , , Eth = A
## 
##    Age
## Sex F0 F1 F2 F3
##   F  5 15  9  9
##   M  8  5 11  7
## 
## , , Eth = N
## 
##    Age
## Sex F0 F1 F2 F3
##   F  5 17 10 10
##   M  9  9 10  7

2.5 cálculos sobre tablas de contigencia

tapply(Days,Age,mean)

##       F0       F1       F2       F3 
## 14.85185 11.15217 21.05000 19.60606

tapply(Days,list(Sex,Age),mean)

##         F0       F1       F2       F3
## F 18.70000 12.96875 18.42105 14.00000
## M 12.58824  7.00000 23.42857 27.21429

tapply(Days,list(Sex,Age),function(x) sqrt(var(x)/length(x)))

##         F0       F1       F2       F3
## F 4.208589 2.329892 5.299959 2.940939
## M 3.768151 1.418093 3.766122 4.569582

2.6 Datos cualitativos

Supongamos unos datos cualquiera de las variables treatment y improvement de pacientes a una enfermedad determinada.

treatment <- factor(rep(c(1, 2), c(43, 41)), levels = c(1, 2),
                    labels = c("placebo", "treated"))
improved <- factor(rep(c(1, 2, 3, 1, 2, 3), c(29, 7, 7, 13, 7, 21)),
                   levels = c(1, 2, 3),
                   labels = c("none", "some", "marked"))

Tabla de contigencia

xtabs(~treatment+improved)

##          improved
## treatment none some marked
##   placebo   29    7      7
##   treated   13    7     21

De manera grfica,

spineplot(improved ~ treatment)

El conjunto de datos de R, UCBAdmissionscontiene los datos agregadps de los solicitantes a universidad de Berkeley a los seis departamentos más grandes en 1973 clasificados por sexo y admisión.

data("UCBAdmissions")
?UCBAdmissions
apply(UCBAdmissions, c(2,1), sum)

##         Admit
## Gender   Admitted Rejected
##   Male       1198     1493
##   Female      557     1278

prop.table(apply(UCBAdmissions, c(2,1), sum))

##         Admit
## Gender    Admitted  Rejected
##   Male   0.2646929 0.3298719
##   Female 0.1230667 0.2823685

ftable(UCBAdmissions)

##                 Dept   A   B   C   D   E   F
## Admit    Gender                             
## Admitted Male        512 353 120 138  53  22
##          Female       89  17 202 131  94  24
## Rejected Male        313 207 205 279 138 351
##          Female       19   8 391 244 299 317

Con ftable podemos presentar la información con mayor claridad

ftable(round(prop.table(UCBAdmissions), 3),
       row.vars="Dept", col.vars = c("Gender", "Admit"))

##      Gender     Male            Female         
##      Admit  Admitted Rejected Admitted Rejected
## Dept                                           
## A              0.113    0.069    0.020    0.004
## B              0.078    0.046    0.004    0.002
## C              0.027    0.045    0.045    0.086
## D              0.030    0.062    0.029    0.054
## E              0.012    0.030    0.021    0.066
## F              0.005    0.078    0.005    0.070

Resulta más intereseante mostrar la información por género Gender y Dept combinados (dimensiones 2 y 3 del array). Nótese que las tasas de admisión por male y female son más o menos similares en todos los departamentos, excepto en “A”, donde las tasas de las mujeres es mayor.

# prop.table(UCBAdmissions, c(2,3))
ftable(round(prop.table(UCBAdmissions, c(2,3)), 2),
       row.vars="Dept", col.vars = c("Gender", "Admit"))

##      Gender     Male            Female         
##      Admit  Admitted Rejected Admitted Rejected
## Dept                                           
## A               0.62     0.38     0.82     0.18
## B               0.63     0.37     0.68     0.32
## C               0.37     0.63     0.34     0.66
## D               0.33     0.67     0.35     0.65
## E               0.28     0.72     0.24     0.76
## F               0.06     0.94     0.07     0.93

## Data aggregated over departments
apply(UCBAdmissions, c(1, 2), sum)

##           Gender
## Admit      Male Female
##   Admitted 1198    557
##   Rejected 1493   1278

gráficamente

spineplot(margin.table(UCBAdmissions, c(3, 2)),
           main = "Applications at UCB")

spineplot(margin.table(UCBAdmissions, c(3, 1)),
           main = "Admissions at UCB")

Estos datos ilustran la denominada paradoja de Simpson. Este hecho ha sido analizado como un posible caso de discriminación por sexo en las tasas de admisión en Berkeley. De los 2691 hombres que solicitaron se admitidos, 1198 (44.5%) fueron admitidos, comparado con las 1835 mujeres de las cuales tan sólo 557 (30.4%) fueron admitidas. Se podría por tanto concluir que los hombres tienes tasas de admisión mayores que las mujeres. Wikipedia: Gender Bias UC Berkeley. See animation at link

2.7 Datos cuantitativos

head(faithful)

##   eruptions waiting
## 1     3.600      79
## 2     1.800      54
## 3     3.333      74
## 4     2.283      62
## 5     4.533      85
## 6     2.883      55

Consideremos los datos del geyse Old Faithful en el parque nacional de Yellowstone, EEUU.

plot(faithful)

2.7.1 Distribuciones de frecuencias

Vamos a utilizar el conjunto de datos faithful, para ilustrar el concepto de distribución de frecuencias que consistirá en crear una series de categorías o intervalos, en los que contaremos el número de observaciones en cada categoría.

duration <- faithful$eruptions
range(duration)

## [1] 1.6 5.1

Crearemos los sub-intervalos entre [1.6, 5.1] y la secuencia { 1.5, 2.0, 2.5, ... }.

breaks <- seq(1.5,5.5,by=0.5)
breaks

## [1] 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

La función cut nos permite divider el rango en los intervalos que especifiquemos, con el argumento right=FALSE, consideramos el intervalo cerrado por la derecha.

duration.cut = cut(duration, breaks, right=FALSE)

Con table generamos las frecuencias

duration.freq = table(duration.cut) 
duration.freq

## duration.cut
## [1.5,2) [2,2.5) [2.5,3) [3,3.5) [3.5,4) [4,4.5) [4.5,5) [5,5.5) 
##      51      41       5       7      30      73      61       4

Con hist podemos realizarlo de manera automática:

freq <- hist(duration)
freq

## $breaks
## [1] 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5
## 
## $counts
## [1] 55 37  5  9 34 75 54  3
## 
## $density
## [1] 0.40441176 0.27205882 0.03676471 0.06617647 0.25000000 0.55147059
## [7] 0.39705882 0.02205882
## 
## $mids
## [1] 1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25
## 
## $xname
## [1] "duration"
## 
## $equidist
## [1] TRUE
## 
## attr(,"class")
## [1] "histogram"

freq <- hist(duration,breaks = breaks)

hist(duration,50)

Estimación de densidad construye una estimación dada una distribucion de probabilidad para una muestra dada.

require(graphics)
d <- density(faithful$eruptions)
d

## 
## Call:
##  density.default(x = faithful$eruptions)
## 
## Data: faithful$eruptions (272 obs.); Bandwidth 'bw' = 0.3348
## 
##        x                y            
##  Min.   :0.5957   Min.   :0.0002262  
##  1st Qu.:1.9728   1st Qu.:0.0514171  
##  Median :3.3500   Median :0.1447010  
##  Mean   :3.3500   Mean   :0.1813462  
##  3rd Qu.:4.7272   3rd Qu.:0.3086071  
##  Max.   :6.1043   Max.   :0.4842095

plot(d)

En dos dimensiones:

library(gplots)
h2 <- hist2d(faithful, nbins=30,xlab="Duration in minutes",ylab="Waiting")

h2

## 
## ----------------------------
## 2-D Histogram Object
## ----------------------------
## 
## Call: hist2d(x = faithful, nbins = 30, xlab = "Duration in minutes", 
##     ylab = "Waiting")
## 
## Number of data points:  272 
## Number of grid bins:  30 x 30 
## X range: ( 1.6 , 5.1 )
## Y range: ( 43 , 96 )

names(h2)

## [1] "counts"   "x.breaks" "y.breaks" "x"        "y"        "nobs"    
## [7] "call"

Frecuencias relativas

duration.relfreq <- duration.freq / nrow(faithful) 
tab <- cbind(duration.freq, duration.relfreq) 
apply(tab,2,sum)

##    duration.freq duration.relfreq 
##              272                1

Distribución de frecuencias acumuladas:

cumsum(duration.freq)

## [1.5,2) [2,2.5) [2.5,3) [3,3.5) [3.5,4) [4,4.5) [4.5,5) [5,5.5) 
##      51      92      97     104     134     207     268     272

cumsum(duration.relfreq)

##   [1.5,2)   [2,2.5)   [2.5,3)   [3,3.5)   [3.5,4)   [4,4.5)   [4.5,5) 
## 0.1875000 0.3382353 0.3566176 0.3823529 0.4926471 0.7610294 0.9852941 
##   [5,5.5) 
## 1.0000000

Estimación bivariante tipo kernel

data("faithful")
attach(faithful)
Dens2d<-kde2d(eruptions,waiting)
image(Dens2d,xlab="eruptions",ylab="waiting")
contour(Dens2d,add=TRUE,col="black",lwd=2,nlevels=5)

detach("faithful")

Gráficos persp

persp(Dens2d,phi=30,theta=20,d=5,xlab="eruptions",ylab="waiting",zlab="",shade=.2,col="lightblue",expand=.85,ticktype = "detailed")

3 Introducción a la programación básico con `R`

3.1 Condicionales

Comparaciones

equal: ==

  "hola" == "hola"

## [1] TRUE

  "hola" == "Hola"

## [1] FALSE

   1 == 2-1

## [1] TRUE

not equal: !=

    a <- c(1,2,4,5)
    b <- c(1,2,3,5) 
    a == b

## [1]  TRUE  TRUE FALSE  TRUE

    a != b

## [1] FALSE FALSE  TRUE FALSE

mayor/menor que: > <

set.seed(1)
a <- rnorm(10)
b <- rnorm(10)
a<b

##  [1]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE

mayor/menor que o igual: >= <=

set.seed(2)
a <- rnorm(10)
b <- rnorm(10)
a >= b

##  [1] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE

which

set.seed(3)
which(a>b)

## [1] 3 6 9

LETTERS

##  [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q"
## [18] "R" "S" "T" "U" "V" "W" "X" "Y" "Z"

which(LETTERS=="R")

## [1] 18

which.min o which.max

set.seed(4)
a <- rnorm(10)
a

##  [1]  0.2167549 -0.5424926  0.8911446  0.5959806  1.6356180  0.6892754
##  [7] -1.2812466 -0.2131445  1.8965399  1.7768632

which.min(a)

## [1] 7

which.max(a)

## [1] 9

is.na

 a[2] <- NA
is.na(a)

##  [1] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

which(is.na(a))

## [1] 2

3.2 Operadores Lógicos

and: &

z = 1:6
which(2 < z & z > 3)

## [1] 4 5 6

or: |

z = 1:6
(z > 2) & (z < 5)

## [1] FALSE FALSE  TRUE  TRUE FALSE FALSE

which((z > 2) & (z < 5))

## [1] 3 4

not: !

x <- c(TRUE,FALSE,0,6)
y <- c(FALSE,TRUE,FALSE,TRUE)

!x

## [1] FALSE  TRUE  TRUE FALSE

Ejemplo:

&& vs &

x&y

## [1] FALSE FALSE FALSE  TRUE

x&&y

## [1] FALSE

|| vs |

x||y

## [1] TRUE

x|y

## [1]  TRUE  TRUE FALSE  TRUE

3.3 `if` statements

if(cond1=true) { cmd1 } else { cmd2 }

if(1==0) {
    print(1)
} else {
    print(2)
}

## [1] 2

3.4 `ifelse`

ifelse(test, true_value, false_value)

x <- 1:10 # Creates sample data
ifelse(x<5 | x>8, x, 0)

##  [1]  1  2  3  4  0  0  0  0  9 10

3.5 `while`

3.6 Loops o Bucles

Los más empleados en R son for, while y apply. Los menos habituales repeat. La función break sirve para salir de un bucle loop.

3.6.1 `for`

Sintaxis

for(variable in sequence) {
    statements
}

for (j in 1:5)
{
  print(j^2)
}

## [1] 1
## [1] 4
## [1] 9
## [1] 16
## [1] 25

Repetir el bucle guardando los resultados en un vector x.

n = 5
x = NULL  # creates a NULL object
for (j in 1:n)
{
  x[j] = j^2
}
x

## [1]  1  4  9 16 25

Generamos el lanzamiento de un dado

nsides = 6
ntrials = 1000
trials = NULL
for (j in 1:ntrials)
{
  trials[j] = sample(1:nsides,1)  # We get one sample at a time
}
mean(trials^2)

## [1] 14.563

Ejemplo:

x <- 1:10
z <- NULL
for(i in seq(along=x)) {
    if (x[i]<5) {
        z <- c(z,x[i]-1) 
    } else {
        stop("values need to be <5")
    }
}
## Error: values need to be <5
z
## [1] 0 1 2 3

3.7 `while`

Similar al bucle for, pero las iteraciones están controladas por una condición.

z <- 0
while(z < 5) {
    z <- z + 2
    print(z) 
}

## [1] 2
## [1] 4
## [1] 6

4 Case studies

4.1 The Forbes 2000 Ranking of the World’s Biggest Companies (Year 2004)

The data handling and manipulation techniques explained will be illustrated by means of a data set of 2000 world leading companies, the Forbes 2000 list for the year 2004 collected by Forbes Magazine. This list is originally available from www.forbes.com

Here we show a subset of the data set:

library("HSAUR2")
data("Forbes2000")

rank	name	country	category	sales	profits	assets	marketvalue
1	Citigroup	United States	Banking	94.71	17.85	1264.03	255.30
2	General Electric	United States	Conglomerates	134.19	15.59	626.93	328.54
3	American Intl Group	United States	Insurance	76.66	6.46	647.66	194.87
4	ExxonMobil	United States	Oil & gas operations	222.88	20.96	166.99	277.02
5	BP	United Kingdom	Oil & gas operations	232.57	10.27	177.57	173.54
6	Bank of America	United States	Banking	49.01	10.81	736.45	117.55

The data consists of 2000 observations on the following 8 variables.

rank: the ranking of the company.
name: the name of the company.
country: a factor giving the country the company is situated in.
category: a factor describing the products the company produces.
sales: the amount of sales of the company in billion USD.
profits: the profit of the company in billion USD.
assets: the assets of the company in billion USD.
marketvalue: the market value of the company in billion USD.

Types of variables

R command

str(Forbes2000)

## 'data.frame':    2000 obs. of  8 variables:
##  $ rank       : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ name       : chr  "Citigroup" "General Electric" "American Intl Group" "ExxonMobil" ...
##  $ country    : Factor w/ 61 levels "Africa","Australia",..: 60 60 60 60 56 60 56 28 60 60 ...
##  $ category   : Factor w/ 27 levels "Aerospace & defense",..: 2 6 16 19 19 2 2 8 9 20 ...
##  $ sales      : num  94.7 134.2 76.7 222.9 232.6 ...
##  $ profits    : num  17.85 15.59 6.46 20.96 10.27 ...
##  $ assets     : num  1264 627 648 167 178 ...
##  $ marketvalue: num  255 329 195 277 174 ...

Factor levels

Nominal measurements are represented by factor variables in R, such as the country of the company or the category of the business segment.

A factor in R is divided into levels

How many countries are on the top 2000 ranking?

R command

nlevels(Forbes2000[,"country"])

## [1] 61

Which countries?

R command

levels(Forbes2000[,"country"])

##  [1] "Africa"                       "Australia"                   
##  [3] "Australia/ United Kingdom"    "Austria"                     
##  [5] "Bahamas"                      "Belgium"                     
##  [7] "Bermuda"                      "Brazil"                      
##  [9] "Canada"                       "Cayman Islands"              
## [11] "Chile"                        "China"                       
## [13] "Czech Republic"               "Denmark"                     
## [15] "Finland"                      "France"                      
## [17] "France/ United Kingdom"       "Germany"                     
## [19] "Greece"                       "Hong Kong/China"             
## [21] "Hungary"                      "India"                       
## [23] "Indonesia"                    "Ireland"                     
## [25] "Islands"                      "Israel"                      
## [27] "Italy"                        "Japan"                       
## [29] "Jordan"                       "Kong/China"                  
## [31] "Korea"                        "Liberia"                     
## [33] "Luxembourg"                   "Malaysia"                    
## [35] "Mexico"                       "Netherlands"                 
## [37] "Netherlands/ United Kingdom"  "New Zealand"                 
## [39] "Norway"                       "Pakistan"                    
## [41] "Panama/ United Kingdom"       "Peru"                        
## [43] "Philippines"                  "Poland"                      
## [45] "Portugal"                     "Russia"                      
## [47] "Singapore"                    "South Africa"                
## [49] "South Korea"                  "Spain"                       
## [51] "Sweden"                       "Switzerland"                 
## [53] "Taiwan"                       "Thailand"                    
## [55] "Turkey"                       "United Kingdom"              
## [57] "United Kingdom/ Australia"    "United Kingdom/ Netherlands" 
## [59] "United Kingdom/ South Africa" "United States"               
## [61] "Venezuela"

And in the top 20?

R commands

top20 <- droplevels(subset(Forbes2000,rank<=20))
levels(top20[,"country"])

## [1] "France"                      "Japan"                      
## [3] "Netherlands"                 "Netherlands/ United Kingdom"
## [5] "Switzerland"                 "United Kingdom"             
## [7] "United States"

As a simple summary statistic, the frequencies of the levels of such a factor variable can be found from

table(top20[,"country"])

## 
##                      France                       Japan 
##                           2                           1 
##                 Netherlands Netherlands/ United Kingdom 
##                           1                           1 
##                 Switzerland              United Kingdom 
##                           1                           3 
##               United States 
##                          11

Which type of companies?

levels(Forbes2000[,"category"])

##  [1] "Aerospace & defense"              "Banking"                         
##  [3] "Business services & supplies"     "Capital goods"                   
##  [5] "Chemicals"                        "Conglomerates"                   
##  [7] "Construction"                     "Consumer durables"               
##  [9] "Diversified financials"           "Drugs & biotechnology"           
## [11] "Food drink & tobacco"             "Food markets"                    
## [13] "Health care equipment & services" "Hotels restaurants & leisure"    
## [15] "Household & personal products"    "Insurance"                       
## [17] "Materials"                        "Media"                           
## [19] "Oil & gas operations"             "Retailing"                       
## [21] "Semiconductors"                   "Software & services"             
## [23] "Technology hardware & equipment"  "Telecommunications services"     
## [25] "Trading companies"                "Transportation"                  
## [27] "Utilities"

How many of each category?

table(Forbes2000[,"category"])

## 
##              Aerospace & defense                          Banking 
##                               19                              313 
##     Business services & supplies                    Capital goods 
##                               70                               53 
##                        Chemicals                    Conglomerates 
##                               50                               31 
##                     Construction                Consumer durables 
##                               79                               74 
##           Diversified financials            Drugs & biotechnology 
##                              158                               45 
##             Food drink & tobacco                     Food markets 
##                               83                               33 
## Health care equipment & services     Hotels restaurants & leisure 
##                               65                               37 
##    Household & personal products                        Insurance 
##                               44                              112 
##                        Materials                            Media 
##                               97                               61 
##             Oil & gas operations                        Retailing 
##                               90                               88 
##                   Semiconductors              Software & services 
##                               26                               31 
##  Technology hardware & equipment      Telecommunications services 
##                               59                               67 
##                Trading companies                   Transportation 
##                               25                               80 
##                        Utilities 
##                              110

A simple summary statistics such as the mean, median, quantiles and range can be found from continuous variables such as sales

R command

summary(Forbes2000[,"sales"])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.010   2.018   4.365   9.697   9.548 256.300

Simple Graphics

Chambers et al. (1983), “there is no statistical tool that is as powerful as a well chosen graph”

Histograms and boxplots

layout(matrix(1:4, nrow = 2,ncol=2))
hist(Forbes2000$marketvalue, col="lightgrey",main="Histogram of market value")
hist(log(Forbes2000$marketvalue),col="lightgrey",main="Histogram of log(market value)")
boxplot(Forbes2000$marketvalue, col="lightgrey",main="Boxplot of market value")
boxplot(log(Forbes2000$marketvalue),col="lightgrey",main="Boxplot of log(market value)")

Scatterplots to visualize the relationship betwen variables

Cool Graphics

Using the ggplot2 library

library(ggplot2)
#?qplot
qplot(marketvalue,data = Forbes2000)

qplot(log(marketvalue),  data = Forbes2000)

qplot(marketvalue,sales, data=Forbes2000)

qplot(log(marketvalue),log(sales),size=assets,alpha = I(0.1),data=Forbes2000)

library(calibrate)
profits_all = na.omit(Forbes2000$profits)  # all_profts without No data
order_profits = order(profits_all)     # index of the profitable companies 
                                       #    in decreasing order
top_50 = rev(order_profits)[1:50]      # top 50 profitable companies

sales = Forbes2000$sales[top_50]       # sales of the 50 top profitable companies
assets = Forbes2000$assets[top_50]     # assets of the 50 top profitable companies
countries = Forbes2000$country[top_50] # countries where the 50 top profitable 
                                       #    companies are found

plot(assets,sales,pch =1)
textxy(assets,sales, abbreviate(countries,2),col = "blue",cex=0.5)  # used to put the 
                                                                    # countries where the companies are found
title(main = "Sales and Assets in billion 
              USD \n of the 50 most profitable companies ", col.main = "gray")

Graphics by factor

Boxplots of the logarithms of the market value for four selected countries, the width of the boxes is proportional to the square roots of the number of companies.

tmp <- subset(Forbes2000,
        country %in% c("United Kingdom", "Germany",
                       "India", "Turkey"))
tmp$country <- tmp$country[,drop = TRUE]
plot(log(marketvalue) ~ country, data = tmp, col = 3:6,
       ylab = "log(marketvalue)", varwidth = TRUE)

Scatterplots by country

library(lattice)
xyplot(log(marketvalue)~log(sales)|country,data=tmp)

4.2 Malignant Melanoma in the USA

Fisher and Belle (1993) report mortality rates due to malignant melanoma of the skin for white males during the period 1950-1969, for each state on the US mainland.

data("USmelanoma",package="HSAUR2")

A data consists of 48 observations on the following 5 variables.

mortality: number of white males died due to malignant melanoma 1950-1969 per one million inhabitants.
latitude: latitude of the geographic centre of the state.
longitude: longitude of the geographic centre of each state.
ocean: a binary variable indicating contiguity to an ocean at levels no or yes.

Plotting mortality rates

xr <- range(USmelanoma$mortality) * c(0.9, 1.1)

Let us plot mortality rates in

#layout(matrix(1:2, nrow = 2))
boxplot(USmelanoma$mortality, ylim = xr, horizontal = TRUE,xlab = "Mortality")

hist(USmelanoma$mortality, xlim = xr, xlab = "", main = "",axes = FALSE, ylab = "")
axis(1)

Malignant melanoma mortality rates by contiguity to an ocean

plot(mortality ~ ocean, data = USmelanoma, xlab = "Contiguity to an ocean", ylab = "Mortality")

Histograms can often be misleading for displaying distributions because of their dependence on the number of classes chosen. An alternative is to formally estimate the density function of a variable and then plot the resulting estimate.

The estimated densities of malignant melanoma mortality rates by contiguity to an ocean looks like this:

dyes <- with(USmelanoma, density(mortality[ocean == "yes"]))
dno <- with(USmelanoma, density(mortality[ocean == "no"]))
plot(dyes, lty = 1, xlim = xr, main = "", ylim = c(0, 0.018))
lines(dno, lty = 2)
legend("topright", lty = 1:2, legend = c("Coastal State","Land State"), bty = "n")

Now we might move on to look at how mortality rates are related to the geographic location of a state as represented by the latitude and longitude of the centre of the state.

layout(matrix(1:2, ncol = 2))
plot(mortality ~ -longitude, data = USmelanoma)
plot(mortality ~ latitude, data = USmelanoma)

4.3 Mapping mortality rates

The data contains the longitude and latitude of the centroids

plot(-USmelanoma$longitude,USmelanoma$latitude,asp=1.5,cex=.3,pch=19,col="blue")

library("sp")
library("maps")
library("maptools")
library("RColorBrewer")
map("state")
points(-USmelanoma$longitude,USmelanoma$latitude,asp=1.5,cex=.3,pch=19,col="blue")

#Create a function to generate a continuous color palette
rbPal <- colorRampPalette(c('blue','grey','red'))
#This adds a column of color values
# based on the y values
USmelanoma$Col <- (rbPal(10)[as.numeric(cut(USmelanoma$mortality,breaks = 10))])
map("state",xlim=c(-135,-65))
points(-USmelanoma$longitude,USmelanoma$latitude,col=USmelanoma$Col,asp=1.5,pch=19,cex=1.2)
legend("topleft",title="Decile",legend=quantile(USmelanoma$mortality,seq(0.1,1,l=10)),col =rbPal(10),pch=15,cex=1.,box.col = NA)

states <- map("state", plot = FALSE, fill = TRUE)
IDs <- sapply(strsplit(states$names, ":"), function(x) x[1])
rownames(USmelanoma) <- tolower(rownames(USmelanoma))

us1 <- map2SpatialPolygons(states, IDs=IDs,proj4string = CRS("+proj=longlat +datum=WGS84"))
us2 <- SpatialPolygonsDataFrame(us1, USmelanoma)

col <- colorRampPalette(c('blue', 'gray80','red'))

spplot(us2, "mortality", col.regions = col(200),par.settings = list(axis.line = list(col =  'transparent')),main="Map of the US showing malignant melanoma mortality rates")

Curso de Estadística básica para Data Scientists

Dae-Jin Lee < lee.daejin@gmail.com >

TEMA 1. Introducción a R

1 Introducción al software estadístico R

1.1 Empezando con R

1.2 Instalar y cargar librerías en R

1.3 Lectura de datos

1.4 Importar datos

1.5 Exportar datos

1.6 Vectores

1.7 Estadística básico

1.8 Vectores caracteres y variables factor

1.9 Data frames

1.10 Trabajando con data frames

1.11 Vectores lógicos

1.12 Trabajando con vectores

1.13 Matrices y arrays

1.14 Factors

1.15 Indexando vectores con condiciones lógicas

1.16 Valores faltantes

1.17 Trabajando con data frames

2 Análisis de datos básico en R

2.1 Gráficos sencillos

2.2 Scatterplots

2.3 más opciones gráficas

2.4 Tablas de clasificación cruzada o de contigencia

2.5 cálculos sobre tablas de contigencia

2.6 Datos cualitativos

2.7 Datos cuantitativos

2.7.1 Distribuciones de frecuencias

3 Introducción a la programación básico con R

3.1 Condicionales

3.2 Operadores Lógicos

3.3 if statements

3.4 ifelse

3.5 while

3.6 Loops o Bucles

3.6.1 for

3.7 while

4 Case studies

4.1 The Forbes 2000 Ranking of the World’s Biggest Companies (Year 2004)

4.2 Malignant Melanoma in the USA

4.3 Mapping mortality rates

TEMA 1. Introducción a `R`

1 Introducción al software estadístico `R`

1.1 Empezando con `R`

1.2 Instalar y cargar librerías en `R`

2 Análisis de datos básico en `R`

3 Introducción a la programación básico con `R`

3.3 `if` statements

3.4 `ifelse`

3.5 `while`

3.6.1 `for`

3.7 `while`